API
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concrete.ml.pytest.utils
: Common functions or lists for test files, which can't be put in fixtures. - ​
concrete.ml.quantization.base_quantized_op
: Base Quantized Op class that implements quantization for a float numpy op. - ​
concrete.ml.quantization.quantized_ops
: Quantized versions of the ONNX operators for post training quantization. - ​
concrete.ml.sklearn.tree_to_numpy
: Implements the conversion of a tree model to a numpy function.
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torch_models.MultiOpOnSingleInputConvNN
: Network that applies two quantized operations on a single input. - ​
torch_models.SimpleQAT
: Torch model implements a step function that needs Greater, Cast and Where. - ​
torch_models.SingleMixNet
: Torch model that with a single conv layer that produces the output, e.g. a blur filter. - ​
torch_models.StepActivationModule
: Torch model implements a step function that needs Greater, Cast and Where. - ​
torch_models.TorchSumMod
: Torch model to test the ReduceSum ONNX operator in a circuit containing a PBS. - ​
base_quantized_op.QuantizedMixingOp
: An operator that mixes (adds or multiplies) together encrypted inputs. - ​
base_quantized_op.QuantizedOpUnivariateOfEncrypted
: An univariate operator of an encrypted value. - ​
quantized_ops.QuantizedBatchNormalization
: Quantized Batch normalization with encrypted input and in-the-clear normalization params. - ​
base.QuantizedTorchEstimatorMixin
: Mixin that provides quantization for a torch module and follows the Estimator API. - ​
qnn.FixedTypeSkorchNeuralNet
: A mixin with a helpful modification to a skorch estimator that fixes the module type. - ​
qnn.QuantizedSkorchEstimatorMixin
: Mixin class that adds quantization features to Skorch NN estimators.
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custom_assert.assert_not_reached
: Provide a custom assert to check that a piece of code is never reached. - ​
utils.check_there_is_no_p_error_options_in_configuration
: Check the user did not set p_error or global_p_error in configuration. - ​
utils.generate_proxy_function
: Generate a proxy function for a function accepting only *args type arguments. - ​
utils.manage_parameters_for_pbs_errors
: Return (p_error, global_p_error) that we want to give to Concrete-Numpy and the compiler. - ​
convert.get_equivalent_numpy_forward
: Get the numpy equivalent forward of the provided ONNX model. - ​
convert.get_equivalent_numpy_forward_and_onnx_model
: Get the numpy equivalent forward of the provided torch Module. - ​
onnx_impl_utils.compute_onnx_pool_padding
: Compute any additional padding needed to compute pooling layers. - ​
onnx_impl_utils.numpy_onnx_pad
: Pad a tensor according to ONNX spec, using an optional custom pad value. - ​
onnx_impl_utils.onnx_avgpool_compute_norm_const
: Compute the average pooling normalization constant. - ​
onnx_model_manipulations.clean_graph_after_node_name
: Clean the graph of the onnx model by removing nodes after the given node name. - ​
onnx_model_manipulations.clean_graph_after_node_op_type
: Clean the graph of the onnx model by removing nodes after the given node type. - ​
onnx_model_manipulations.keep_following_outputs_discard_others
: Keep the outputs given in outputs_to_keep and remove the others from the model. - ​
onnx_model_manipulations.remove_unused_constant_nodes
: Remove unused Constant nodes in the provided onnx model. - ​
onnx_model_manipulations.simplify_onnx_model
: Simplify an ONNX model, removes unused Constant nodes and Identity nodes. - ​
ops_impl.numpy_greater_float
: Compute greater in numpy according to ONNX spec and cast outputs to floats. - ​
ops_impl.numpy_greater_or_equal_float
: Compute greater or equal in numpy according to ONNX specs and cast outputs to floats. - ​
ops_impl.numpy_less_float
: Compute less in numpy according to ONNX spec and cast outputs to floats. - ​
ops_impl.numpy_less_or_equal_float
: Compute less or equal in numpy according to ONNX spec and cast outputs to floats. - ​
ops_impl.numpy_not_float
: Compute not in numpy according to ONNX spec and cast outputs to floats. - ​
ops_impl.onnx_func_raw_args
: Decorate a numpy onnx function to flag the raw/non quantized inputs. - ​
base.get_sklearn_neural_net_models
: Return the list of available neural net models in Concrete-ML. - ​
compile.convert_torch_tensor_or_numpy_array_to_numpy_array
: Convert a torch tensor or a numpy array to a numpy array.
Last modified 19d ago